Object attitude determination from two-dimensional images has been a challenging goal for a variety of applications, ranging from commercial applications, such as video inspection and manipulation of objects traveling on a conveyor line, to military applications, such as recognizing the attitude of friendly or hostile vehicles. In particular, such object attitude determination is important in autonomous systems, such as unmanned orbiter systems, which are required to interact and/or dock with satellites or other objects using only cameras or other instruments that only produce two-dimensional images. Furthermore, such attitude determination may be particularly useful in confirming proper attitude approach of an aircraft to an aircraft carrier during inclement weather.
A first known method of determining the attitude of an object involves “edge-determination” of the object and subsequent corner recognition for the intersecting edges. That is, by determining where critical corners occur in an image, the orientation of the object can be calculated. This method works best for simple objects with clearly defined straight edges. It may be important to note that the object used to develop this method in the present reference was a tetrahedron. However, this method has the disadvantage that objects with ill-defined or rounded edges, or complicated objects with many edges may be difficult or impossible to properly analyze. Furthermore, objects that cast shadows can also create artificial edges which cut across the object, which can degrade the performance and accuracy of this approach. Still further, this method is computationally intensive.
A second known method of determining the attitude of an object utilizes a set of training images representing the object to be evaluated under various conditions of illumination, orientation, and occlusion. This training is followed by representing new input images as projected points in the eigenimage space and applying a matrix to determine correspondence with an image from the training image set. Eigenvectors of the covariance matrix of the training image set are used as weight vectors to apply to the measurement matrix for determining which training image best fits an input image. This approach is designed to discriminate between widely divergent views of an object. However, because the training images include similar views of the object at a variety of attitudes and illumination views, which are developed by neural network analysis, it is not well-suited for precise attitude determination. Similar to the first known method, this method is computationally intense.
Accordingly, there exists a need in the relevant art to provide a method of attitude determination of an object from a two-dimensional image that provides sufficient accuracy to enable autonomous operation of a vehicle. Furthermore, there exists a need in the relevant art to provide a method of attitude determination of an object from a two-dimensional image that minimizes the computational requirements needed. Still further, there exists a need in the relevant art to provide a method of attitude determination of an object from a two-dimensional image that overcomes the disadvantages of the prior art.